Decisions about visual stimuli are frequently shaped by inputs from other sensory modalities. The goal of this proposal is to gain a deeper understanding of the neural mechanisms that enable integration of visual and auditory inputs for decision-making. Behavioral experiments have established that subjects can integrate information across sensory modalities to make better decisions. Further, subjects weight each sensory modality in proportion to its reliability. Experimenters can estimate subjects' perceptual weights by presenting conflicting information to two sensory modalities and examining the degree to which decisions are biased toward one modality or the other. Despite a wealth of data about the behavioral consequences of multisensory stimuli, much remains unknown about the underlying neural mechanisms. By collecting electrophysiological and behavioral data together, we are in an ideal position to connect multisensory decision-making to its underlying neural mechanism. My central hypothesis is that neurons will show greater stimulus-driven modulation for multisensory stimuli than for unisensory stimuli and that the neural weights that we estimate by comparing unisensory and multisensory responses will be similar for reliable and unreliable stimuli. The posterior parietal cortex is a candidate area for supporting multisensory improvements in rats: it receives inputs from auditory/visual areas and plays a role in motor planning. In Aim 1, we will estimate humans' and rats' perceptual weights on a novel decision-making task in which subjects judge the overall rate of a stream of events: i.e. flashes, clicks or both together. In Aim 2, we will collect electrophysiological data from posterior parietal corte of rats while they are engaged in the task to establish that responses reflect decision-related activity. In Aim 3, we will measure responses for unisensory vs. multisensory stimuli at different levels of stimulus reliability. For each level of reliability, we will estimate the neural weights that describe the degree of stimulus-driven modulation for auditory vs. visual inputs. If the neural weights, like the perceptual weights, change with reliability, this would suggest that stimulus reliability is explicitly encoded by single neurons. If, instead, the neural weights remain unchanged for high- vs. low-reliability stimuli, this would suggest that reliability is automaticaly encoded by a population response that naturally become more variable when it has a lower gain. The latter possibility is predicted by an optimal model of decision-making.